A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Posterior regularization enhances Bayesian hierarchical mixture clustering by improving node separation.
The paper explains implicit regularization in hierarchical tensor factorization and deep CNNs.
Improved prediction of hierarchical time series using structured regularization.
Paper introduces hierarchical softmax for global hierarchical classification tasks.
A new method for linear regression using feature graphs and hierarchical shrinkage.
Diffusion models generalize better with hierarchical data structure and regularization.
Hyperbolic space outperforms Euclidean in learning hierarchical data.
Dropout is a very effective method in preventing overfitting and has become the go-to regularizer for multi-layer neural networks in recent years. Hierarchical mixture of experts is a hierarchically gated model that defines a soft decision tree where leaves correspond to experts and decision nodes correspond to gating …
Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures. The former lead to low computational time but require the selection of a regularization parameter, whose value significantly impacts the achieved solution, and whos…
Sparse model for noisy datasets using hierarchical regularization.
CoRe method improves time series forecasting coherency without strict constraints.
The paper extends NSGPs with -regularization for sparsity and solves the resulting R-NSGP regression problem.
An important problem in multi-label classification is to capture label patterns or underlying structures that have an impact on such patterns. This paper addresses one such problem, namely how to exploit hierarchical structures over labels. We present a novel method to learn vector representations of a label space give…
Deep neural networks have been shown to be very successful at learning feature hierarchies in supervised learning tasks. Generative models, on the other hand, have benefited less from hierarchical models with multiple layers of latent variables. In this paper, we prove that hierarchical latent variable models do not ta…
Method solves learning problem with hierarchical control objectives.
A new convolutional spectral kernel network learns hierarchical and local features.
Variational dropout (VD) is a generalization of Gaussian dropout, which aims at inferring the posterior of network weights based on a log-uniform prior on them to learn these weights as well as dropout rate simultaneously. The log-uniform prior not only interprets the regularization capacity of Gaussian dropout in netw…
HEBAE improves VAEs by adaptively balancing reconstruction and regularization.
Paper proposes a Renyi entropy-based method for tuning hierarchical topic models.
The paper develops a decision support system for hierarchical text classification of conference proceedings.
A new method for accurately reconstructing signals without knowing the kernel or signal regularity.
Proves EM algorithm guarantees for hierarchical imitation learning.
Hierarchical decoupling improves sample efficiency for complex robots.
Hierarchical graph clustering is a common technique to reveal the multi-scale structure of complex networks. We propose a novel metric for assessing the quality of a hierarchical clustering. This metric reflects the ability to reconstruct the graph from the dendrogram, which encodes the hierarchy. The optimal represent…
A new framework generates large hierarchical search spaces for neural architectures.
Paper analyzes consistency of Bayesian and machine learning methods for hierarchical parameter estimation.
We present Multitask Soft Option Learning(MSOL), a hierarchical multitask framework based on Planning as Inference. MSOL extends the concept of options, using separate variational posteriors for each task, regularized by a shared prior. This ''soft'' version of options avoids several instabilities during training in a …
We propose a new method for learning word representations using hierarchical regularization in sparse coding inspired by the linguistic study of word meanings. We show an efficient learning algorithm based on stochastic proximal methods that is significantly faster than previous approaches, making it possible to perfor…
The staircase property aids deep learning by guiding hierarchical feature learning.
Convex clustering is a recent stable alternative to hierarchical clustering. It formulates the recovery of progressively coalescing clusters as a regularized convex problem. While convex clustering was originally designed for handling Euclidean distances between data points, in a growing number of applications, the dat…
DHRL learns interpretable features from visual data.
We propose a new class of convex penalty functions, called \emph{variational Gram functions} (VGFs), that can promote pairwise relations, such as orthogonality, among a set of vectors in a vector space. These functions can serve as regularizers in convex optimization problems arising from hierarchical classification, m…
NVAE improves VAE performance on large image datasets.
New algorithms adaptively compete against complex environments with local regularities.
Hierarchical clustering is a popular unsupervised data analysis method. For many real-world applications, we would like to exploit prior information about the data that imposes constraints on the clustering hierarchy, and is not captured by the set of features available to the algorithm. This gives rise to the problem …
gKRLS accelerates KRLS estimation for complex models.
Decision making is a process that is extremely prone to different biases. In this paper we consider learning fair representations that aim at removing nuisance (sensitive) information from the decision process. For this purpose, we propose to use deep generative modeling and adapt a hierarchical Variational Auto-Encode…
A novel hierarchical Bayesian approach to Federated Learning reduces data exposure and improves convergence rates.
A theoretical framework for deep learning is proposed to explain its effectiveness.
This paper fortifies the recently introduced hierarchical-optimization recursive least squares (HO-RLS) against outliers which contaminate infrequently linear-regression models. Outliers are modeled as nuisance variables and are estimated together with the linear filter/system variables via a sparsity-inducing (non-)co…
HS improves tree-based models' accuracy and interpretability without changing their structure.
In this paper, we present Gamma-LSTM, an enhanced long short term memory (LSTM) unit, to enable learning of hierarchical representations through multiple stages of temporal abstractions. Gamma memory, a hierarchical memory unit, forms the central memory of Gamma-LSTM with gates to regulate the information flow into var…
Inverse inference, or "brain reading", is a recent paradigm for analyzing functional magnetic resonance imaging (fMRI) data, based on pattern recognition and statistical learning. By predicting some cognitive variables related to brain activation maps, this approach aims at decoding brain activity. Inverse inference ta…
Hierarchical clustering is a class of algorithms that seeks to build a hierarchy of clusters. It has been the dominant approach to constructing embedded classification schemes since it outputs dendrograms, which capture the hierarchical relationship among members at all levels of granularity, simultaneously. Being gree…
Variable selection for models including interactions between explanatory variables often needs to obey certain hierarchical constraints. The weak or strong structural hierarchy requires that the existence of an interaction term implies at least one or both associated main effects to be present in the model. Lately, thi…
New method automates asymmetric choice for better skill transfer in reinforcement learning.
We construct a deep portfolio theory. By building on Markowitz's classic risk-return trade-off, we develop a self-contained four-step routine of encode, calibrate, validate and verify to formulate an automated and general portfolio selection process. At the heart of our algorithm are deep hierarchical compositions of p…
Entropy-based model for hierarchical learning from multiscale data.